Social Welfare Policy: Social Rehabilitation of Psychiatric Patients in Urban china
Bibliographic record
Abstract
BACKGROUND: The background of this paper is an empirical research on social rehabilitation of psychiatric patients in a large urban city in China during the post-Mao period, the Beijing Psychiatric Rehabilitation Research. Another aspect of this background is an exchange with Chen Sheying, a colleague interested in social services for the elderly in China. The underlying assumption of this paper is the multiple similarities between those two areas. OBJECTIVES: The first objective of this paper is to present a contextual analysis of the development of psychiatric rehabilitation in urban China and a second objective is to stress the similarities between psychiatric rehabilitation and social services to the elderly. MATERIAL: The material presented, while referring mainly to the general context of psychiatry and rehabilitation around that period, includes some data from the Beijing research. There are five analytical dimensions: (1) epistemological choices and research paradigms; (2) rehabilitation as an idea; (3) rehabilitation as a social, political and cultural matter; (4) factors of change in the recent history of China; and, finally, (5) mental illness as a personal experience. DISCUSSION: This presentation leads to a discussion about the multiple similarities between the social welfare of two vulnerable categories of people (i.e. psychiatric patients and the elderly). It also offers, in the specific field of mental illness, a general interpretation of the rapid social changes in urban China. CONCLUSION: The conclusion is that psychiatric and ageing services are both a product of interaction among various cultural and social-political-economic factors. Any social welfare intervention or policy should be based on a thorough understanding of the five dimensions referred to earlier, including the traditional Chinese familism and structural dimensions of the post-Mao 'economic state' orientation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".